The Model-Based Archaeology of Socionatural Systems

While the reviewer is generally in favour of the approaches and models outlined in these volumes, one fragment of the review jumped out at me:

There seems to be an uncritical assumption that the more detail that is included, the better the model. There is little evidence that the authors are familiar with the attempts of the ABSS community to clarify such matters as validation, sensitivity analysis and parameter space exploration, and the critical task of choosing an appropriate level of abstraction (or granularity) for a model.

Now, that was the only really negative comment, but it is one that applies to archaeological modeling in general. I mention it here, because my own models have been criticized for not having enough detail. That is however the point. My models tend to be more abstract than the average archaeological simulation, and so I agree with the reviewer: more detail is not necessarily better. In fact, more detail can often paralyze the analysis.

Model building is about simplification, about understanding from the gaps as much as from the content.

This lecture treats some enduring misconceptions about modeling. One of these is that the goal is always prediction. The lecture distinguishes between explanation and prediction as modeling goals, and offers sixteen reasons other than prediction to build a model. It also challenges the common assumption that scientific theories arise from and ‘summarize’ data, when often, theories precede and guide data collection; without theory, in other words, it is not clear what data to collect. Among other things, it also argues that the modeling enterprise enforces habits of mind essential to freedom. It is based on the author’s 2008 Bastille Day keynote address to the Second World Congress on Social Simulation, George Mason University, and earlier addresses at the Institute of Medicine, the University of Michigan, and the Santa Fe Institute.